Overview

Dataset statistics

Number of variables16
Number of observations1042774
Missing cells181352
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.3 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 42149 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 45338 (4.3%) missing values Missing
jerseyNumber has 45338 (4.3%) missing values Missing
o has 45338 (4.3%) missing values Missing
dir has 45338 (4.3%) missing values Missing
s has 64324 (6.2%) zeros Zeros
a has 60212 (5.8%) zeros Zeros
dis has 64432 (6.2%) zeros Zeros

Reproduction

Analysis started2022-11-02 15:03:36.215598
Analysis finished2022-11-02 15:05:09.792761
Duration1 minute and 33.58 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021091890
Minimum2021091600
Maximum2021092000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:09.840259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021091600
5-th percentile2021091600
Q12021091903
median2021091906
Q32021091910
95-th percentile2021092000
Maximum2021092000
Range400
Interquartile range (IQR)7

Descriptive statistics

Standard deviation83.08845928
Coefficient of variation (CV)4.111067868 × 10-8
Kurtosis7.716409658
Mean2021091890
Median Absolute Deviation (MAD)4
Skewness-2.87491269
Sum2.107542075 × 1015
Variance6903.692065
MonotonicityIncreasing
2022-11-02T12:05:09.935979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
202109190575923
 
7.3%
202109191074520
 
7.1%
202109191273784
 
7.1%
202109160073761
 
7.1%
202109190471346
 
6.8%
202109191168310
 
6.6%
202109190367850
 
6.5%
202109190666539
 
6.4%
202109190065780
 
6.3%
202109190865504
 
6.3%
Other values (6)339457
32.6%
ValueCountFrequency (%)
202109160073761
7.1%
202109190065780
6.3%
202109190162100
6.0%
202109190251819
5.0%
202109190367850
6.5%
202109190471346
6.8%
202109190575923
7.3%
202109190666539
6.4%
202109190751451
4.9%
202109190865504
6.3%
ValueCountFrequency (%)
202109200060559
5.8%
202109191354533
5.2%
202109191273784
7.1%
202109191168310
6.6%
202109191074520
7.1%
202109190958995
5.7%
202109190865504
6.3%
202109190751451
4.9%
202109190666539
6.4%
202109190575923
7.3%

playId
Real number (ℝ≥0)

Distinct943
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2060.790242
Minimum54
Maximum4574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:10.220416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile238
Q11091
median2060
Q33049
95-th percentile3836
Maximum4574
Range4520
Interquartile range (IQR)1958

Descriptive statistics

Standard deviation1162.202997
Coefficient of variation (CV)0.5639598701
Kurtosis-1.098271353
Mean2060.790242
Median Absolute Deviation (MAD)979
Skewness0.03410623412
Sum2148938484
Variance1350715.807
MonotonicityNot monotonic
2022-11-02T12:05:10.342979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11433933
 
0.4%
20103910
 
0.4%
763312
 
0.3%
25313197
 
0.3%
5233151
 
0.3%
33363105
 
0.3%
20983013
 
0.3%
16712967
 
0.3%
15682898
 
0.3%
37292875
 
0.3%
Other values (933)1010413
96.9%
ValueCountFrequency (%)
541035
 
0.1%
551035
 
0.1%
591610
0.2%
621311
 
0.1%
65874
 
0.1%
75989
 
0.1%
763312
0.3%
77966
 
0.1%
82644
 
0.1%
95989
 
0.1%
ValueCountFrequency (%)
45741403
0.1%
4552851
0.1%
4530989
0.1%
4518644
0.1%
44891587
0.2%
4445966
0.1%
4431736
0.1%
4409828
0.1%
4380874
0.1%
4345874
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1156
Distinct (%)0.1%
Missing45338
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45570.49178
Minimum25511
Maximum53957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:10.470891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile37130
Q142410
median45011
Q347971
95-th percentile53465
Maximum53957
Range28446
Interquartile range (IQR)5561

Descriptive statistics

Standard deviation5013.50095
Coefficient of variation (CV)0.110016389
Kurtosis-0.03850193068
Mean45570.49178
Median Absolute Deviation (MAD)2842
Skewness-0.1623338934
Sum4.545364904 × 1010
Variance25135191.78
MonotonicityNot monotonic
2022-11-02T12:05:10.591673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400891972
 
0.2%
534421972
 
0.2%
413901972
 
0.2%
419591972
 
0.2%
434781972
 
0.2%
524591972
 
0.2%
524141972
 
0.2%
456301972
 
0.2%
435331945
 
0.2%
524261914
 
0.2%
Other values (1146)977801
93.8%
(Missing)45338
 
4.3%
ValueCountFrequency (%)
255111377
0.1%
289631287
0.1%
295501458
0.1%
298511071
0.1%
30842467
 
< 0.1%
308691325
0.1%
330841792
0.2%
331071399
0.1%
33131650
 
0.1%
33566212
 
< 0.1%
ValueCountFrequency (%)
53957843
0.1%
5395389
 
< 0.1%
53946111
 
< 0.1%
5393042
 
< 0.1%
5387672
 
< 0.1%
53861144
 
< 0.1%
5368765
 
< 0.1%
53679140
 
< 0.1%
536741335
0.1%
53668253
 
< 0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.55677357
Minimum1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:10.724013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile51
Maximum171
Range170
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.39530126
Coefficient of variation (CV)0.695990952
Kurtosis7.788103265
Mean23.55677357
Median Absolute Deviation (MAD)11
Skewness1.647163031
Sum24564391
Variance268.8059035
MonotonicityNot monotonic
2022-11-02T12:05:10.846560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124541
 
2.4%
1224541
 
2.4%
1924541
 
2.4%
1824541
 
2.4%
1724541
 
2.4%
1624541
 
2.4%
1524541
 
2.4%
1424541
 
2.4%
1324541
 
2.4%
1124541
 
2.4%
Other values (161)797364
76.5%
ValueCountFrequency (%)
124541
2.4%
224541
2.4%
324541
2.4%
424541
2.4%
524541
2.4%
624541
2.4%
724541
2.4%
824541
2.4%
924541
2.4%
1024541
2.4%
ValueCountFrequency (%)
17123
< 0.1%
17046
< 0.1%
16946
< 0.1%
16846
< 0.1%
16746
< 0.1%
16646
< 0.1%
16546
< 0.1%
16446
< 0.1%
16346
< 0.1%
16246
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct42149
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
2021-09-19T17:05:13.800
 
69
2021-09-19T17:22:54.400
 
69
2021-09-19T21:04:55.300
 
69
2021-09-19T18:02:59.100
 
69
2021-09-19T21:04:55.100
 
69
Other values (42144)
1042429 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23983802
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2021-09-17T00:23:09.600
2nd row2021-09-17T00:23:09.700
3rd row2021-09-17T00:23:09.800
4th row2021-09-17T00:23:09.900
5th row2021-09-17T00:23:10.000

Common Values

ValueCountFrequency (%)
2021-09-19T17:05:13.80069
 
< 0.1%
2021-09-19T17:22:54.40069
 
< 0.1%
2021-09-19T21:04:55.30069
 
< 0.1%
2021-09-19T18:02:59.10069
 
< 0.1%
2021-09-19T21:04:55.10069
 
< 0.1%
2021-09-19T21:04:55.00069
 
< 0.1%
2021-09-19T21:04:54.90069
 
< 0.1%
2021-09-19T17:22:54.50069
 
< 0.1%
2021-09-19T17:22:54.30069
 
< 0.1%
2021-09-19T21:04:55.50069
 
< 0.1%
Other values (42139)1042084
99.9%

Length

2022-11-02T12:05:10.964732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-09-19t17:05:13.80069
 
< 0.1%
2021-09-19t21:04:55.20069
 
< 0.1%
2021-09-19t19:43:26.30069
 
< 0.1%
2021-09-19t17:49:04.70069
 
< 0.1%
2021-09-19t17:49:04.60069
 
< 0.1%
2021-09-19t17:49:04.50069
 
< 0.1%
2021-09-19t17:49:04.40069
 
< 0.1%
2021-09-19t19:43:26.90069
 
< 0.1%
2021-09-19t19:43:26.80069
 
< 0.1%
2021-09-19t19:43:26.70069
 
< 0.1%
Other values (42139)1042084
99.9%

Most occurring characters

ValueCountFrequency (%)
05203794
21.7%
13393972
14.2%
23311122
13.8%
92410585
10.1%
-2085548
8.7%
:2085548
8.7%
T1042774
 
4.3%
.1042774
 
4.3%
3733078
 
3.1%
5651498
 
2.7%
Other values (4)2023109
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17727158
73.9%
Other Punctuation3128322
 
13.0%
Dash Punctuation2085548
 
8.7%
Uppercase Letter1042774
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05203794
29.4%
13393972
19.1%
23311122
18.7%
92410585
13.6%
3733078
 
4.1%
5651498
 
3.7%
4624890
 
3.5%
7591860
 
3.3%
8499769
 
2.8%
6306590
 
1.7%
Other Punctuation
ValueCountFrequency (%)
:2085548
66.7%
.1042774
33.3%
Dash Punctuation
ValueCountFrequency (%)
-2085548
100.0%
Uppercase Letter
ValueCountFrequency (%)
T1042774
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22941028
95.7%
Latin1042774
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
05203794
22.7%
13393972
14.8%
23311122
14.4%
92410585
10.5%
-2085548
9.1%
:2085548
9.1%
.1042774
 
4.5%
3733078
 
3.2%
5651498
 
2.8%
4624890
 
2.7%
Other values (3)1398219
 
6.1%
Latin
ValueCountFrequency (%)
T1042774
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23983802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05203794
21.7%
13393972
14.2%
23311122
13.8%
92410585
10.1%
-2085548
8.7%
:2085548
8.7%
T1042774
 
4.3%
.1042774
 
4.3%
3733078
 
3.1%
5651498
 
2.7%
Other values (4)2023109
 
8.4%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)< 0.1%
Missing45338
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean49.57896045
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:11.076030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median52
Q375
95-th percentile96
Maximum99
Range98
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.93014313
Coefficient of variation (CV)0.6036863794
Kurtosis-1.336541678
Mean49.57896045
Median Absolute Deviation (MAD)27
Skewness0.03295036995
Sum49451840
Variance895.8134678
MonotonicityNot monotonic
2022-11-02T12:05:11.209169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221758
 
2.1%
2120602
 
2.0%
2620253
 
1.9%
2319989
 
1.9%
7619780
 
1.9%
2418824
 
1.8%
1118607
 
1.8%
5417228
 
1.7%
9116167
 
1.6%
7215557
 
1.5%
Other values (89)808671
77.5%
(Missing)45338
 
4.3%
ValueCountFrequency (%)
111349
1.1%
221758
2.1%
36356
 
0.6%
48654
 
0.8%
56079
 
0.6%
67970
 
0.8%
77866
 
0.8%
814613
1.4%
97380
 
0.7%
1013886
1.3%
ValueCountFrequency (%)
9914095
1.4%
9813717
1.3%
9715532
1.5%
969868
0.9%
959767
0.9%
9413500
1.3%
939696
0.9%
926617
0.6%
9116167
1.6%
9013880
1.3%

team
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
football
 
45338
BUF
 
36311
MIA
 
36311
ATL
 
35640
TB
 
35640
Other values (28)
853534 

Length

Max length8
Median length3
Mean length2.984980446
Min length2

Characters and Unicode

Total characters3112660
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYG
2nd rowNYG
3rd rowNYG
4th rowNYG
5th rowNYG

Common Values

ValueCountFrequency (%)
football45338
 
4.3%
BUF36311
 
3.5%
MIA36311
 
3.5%
ATL35640
 
3.4%
TB35640
 
3.4%
TEN35288
 
3.4%
SEA35288
 
3.4%
NYG35277
 
3.4%
WAS35277
 
3.4%
DEN34122
 
3.3%
Other values (23)678282
65.0%

Length

2022-11-02T12:05:11.329149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football45338
 
4.3%
buf36311
 
3.5%
mia36311
 
3.5%
atl35640
 
3.4%
tb35640
 
3.4%
ten35288
 
3.4%
sea35288
 
3.4%
nyg35277
 
3.4%
was35277
 
3.4%
den34122
 
3.3%
Other values (23)678282
65.0%

Most occurring characters

ValueCountFrequency (%)
A360184
 
11.6%
N290158
 
9.3%
I240526
 
7.7%
L215622
 
6.9%
E190267
 
6.1%
C174394
 
5.6%
T166859
 
5.4%
D128205
 
4.1%
B126995
 
4.1%
S95172
 
3.1%
Other values (20)1124278
36.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2749956
88.3%
Lowercase Letter362704
 
11.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A360184
13.1%
N290158
 
10.6%
I240526
 
8.7%
L215622
 
7.8%
E190267
 
6.9%
C174394
 
6.3%
T166859
 
6.1%
D128205
 
4.7%
B126995
 
4.6%
S95172
 
3.5%
Other values (14)761574
27.7%
Lowercase Letter
ValueCountFrequency (%)
l90676
25.0%
o90676
25.0%
f45338
12.5%
a45338
12.5%
b45338
12.5%
t45338
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3112660
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A360184
 
11.6%
N290158
 
9.3%
I240526
 
7.7%
L215622
 
6.9%
E190267
 
6.1%
C174394
 
5.6%
T166859
 
5.4%
D128205
 
4.1%
B126995
 
4.1%
S95172
 
3.1%
Other values (20)1124278
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3112660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A360184
 
11.6%
N290158
 
9.3%
I240526
 
7.7%
L215622
 
6.9%
E190267
 
6.1%
C174394
 
5.6%
T166859
 
5.4%
D128205
 
4.1%
B126995
 
4.1%
S95172
 
3.1%
Other values (20)1124278
36.1%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
right
523526 
left
519248 

Length

Max length5
Median length5
Mean length4.502051259
Min length4

Characters and Unicode

Total characters4694622
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
right523526
50.2%
left519248
49.8%

Length

2022-11-02T12:05:11.423819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T12:05:11.518026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
right523526
50.2%
left519248
49.8%

Most occurring characters

ValueCountFrequency (%)
t1042774
22.2%
r523526
11.2%
i523526
11.2%
g523526
11.2%
h523526
11.2%
l519248
11.1%
e519248
11.1%
f519248
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4694622
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1042774
22.2%
r523526
11.2%
i523526
11.2%
g523526
11.2%
h523526
11.2%
l519248
11.1%
e519248
11.1%
f519248
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4694622
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1042774
22.2%
r523526
11.2%
i523526
11.2%
g523526
11.2%
h523526
11.2%
l519248
11.1%
e519248
11.1%
f519248
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4694622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1042774
22.2%
r523526
11.2%
i523526
11.2%
g523526
11.2%
h523526
11.2%
l519248
11.1%
e519248
11.1%
f519248
11.1%

x
Real number (ℝ≥0)

Distinct11548
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.5512206
Minimum1.23
Maximum120.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:11.613093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.23
5-th percentile21.45
Q141.03
median58.96
Q378.04
95-th percentile98.42
Maximum120.01
Range118.78
Interquartile range (IQR)37.01

Descriptive statistics

Standard deviation23.76694891
Coefficient of variation (CV)0.3991009533
Kurtosis-0.8100265111
Mean59.5512206
Median Absolute Deviation (MAD)18.48
Skewness0.04421791045
Sum62098464.51
Variance564.8678606
MonotonicityNot monotonic
2022-11-02T12:05:11.743690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.5233
 
< 0.1%
69.14233
 
< 0.1%
69.58226
 
< 0.1%
70.67225
 
< 0.1%
55.34224
 
< 0.1%
69.2215
 
< 0.1%
69.08215
 
< 0.1%
56.76212
 
< 0.1%
57.45205
 
< 0.1%
57.84204
 
< 0.1%
Other values (11538)1040582
99.8%
ValueCountFrequency (%)
1.231
< 0.1%
1.391
< 0.1%
1.471
< 0.1%
1.561
< 0.1%
1.661
< 0.1%
1.71
< 0.1%
1.731
< 0.1%
1.821
< 0.1%
1.921
< 0.1%
1.951
< 0.1%
ValueCountFrequency (%)
120.011
< 0.1%
1202
< 0.1%
119.972
< 0.1%
119.911
< 0.1%
119.91
< 0.1%
119.811
< 0.1%
119.71
< 0.1%
119.561
< 0.1%
119.491
< 0.1%
119.481
< 0.1%

y
Real number (ℝ)

Distinct5347
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.72224564
Minimum-3.42
Maximum53.28
Zeros1
Zeros (%)< 0.1%
Negative79
Negative (%)< 0.1%
Memory size8.0 MiB
2022-11-02T12:05:11.875849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.42
5-th percentile11.63
Q121.98
median26.76
Q331.55
95-th percentile41.53
Maximum53.28
Range56.7
Interquartile range (IQR)9.57

Descriptive statistics

Standard deviation8.249658559
Coefficient of variation (CV)0.3087187607
Kurtosis0.3276084201
Mean26.72224564
Median Absolute Deviation (MAD)4.78
Skewness-0.03129551577
Sum27865262.98
Variance68.05686635
MonotonicityNot monotonic
2022-11-02T12:05:11.995964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.691025
 
0.1%
23.751012
 
0.1%
23.73998
 
0.1%
23.85990
 
0.1%
29.79981
 
0.1%
23.67979
 
0.1%
23.78979
 
0.1%
29.61977
 
0.1%
23.83977
 
0.1%
29.76972
 
0.1%
Other values (5337)1032884
99.1%
ValueCountFrequency (%)
-3.421
< 0.1%
-3.151
< 0.1%
-3.051
< 0.1%
-3.011
< 0.1%
-2.971
< 0.1%
-2.931
< 0.1%
-2.891
< 0.1%
-2.881
< 0.1%
-2.831
< 0.1%
-2.791
< 0.1%
ValueCountFrequency (%)
53.281
< 0.1%
53.211
< 0.1%
53.191
< 0.1%
53.182
< 0.1%
53.171
< 0.1%
53.162
< 0.1%
53.151
< 0.1%
53.131
< 0.1%
53.111
< 0.1%
53.082
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2136
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.591271532
Minimum0
Maximum27.93
Zeros64324
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:12.124658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.77
median2.14
Q33.83
95-th percentile6.78
Maximum27.93
Range27.93
Interquartile range (IQR)3.06

Descriptive statistics

Standard deviation2.388592779
Coefficient of variation (CV)0.9217840546
Kurtosis14.59087122
Mean2.591271532
Median Absolute Deviation (MAD)1.5
Skewness2.36346026
Sum2702110.58
Variance5.705375464
MonotonicityNot monotonic
2022-11-02T12:05:12.240104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064324
 
6.2%
0.0114914
 
1.4%
0.028612
 
0.8%
0.036627
 
0.6%
0.045641
 
0.5%
0.055183
 
0.5%
0.064652
 
0.4%
0.074259
 
0.4%
0.093915
 
0.4%
0.083893
 
0.4%
Other values (2126)920754
88.3%
ValueCountFrequency (%)
064324
6.2%
0.0114914
 
1.4%
0.028612
 
0.8%
0.036627
 
0.6%
0.045641
 
0.5%
0.055183
 
0.5%
0.064652
 
0.4%
0.074259
 
0.4%
0.083893
 
0.4%
0.093915
 
0.4%
ValueCountFrequency (%)
27.931
< 0.1%
27.741
< 0.1%
27.621
< 0.1%
27.551
< 0.1%
27.52
< 0.1%
27.421
< 0.1%
27.371
< 0.1%
27.321
< 0.1%
27.251
< 0.1%
27.151
< 0.1%

a
Real number (ℝ≥0)

ZEROS

Distinct1552
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.790390353
Minimum0
Maximum33.56
Zeros60212
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:12.366101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.71
median1.53
Q32.58
95-th percentile4.47
Maximum33.56
Range33.56
Interquartile range (IQR)1.87

Descriptive statistics

Standard deviation1.444250141
Coefficient of variation (CV)0.8066677405
Kurtosis7.673518942
Mean1.790390353
Median Absolute Deviation (MAD)0.91
Skewness1.520473414
Sum1866972.51
Variance2.085858469
MonotonicityNot monotonic
2022-11-02T12:05:12.488444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060212
 
5.8%
0.0111568
 
1.1%
0.026531
 
0.6%
0.035014
 
0.5%
0.044188
 
0.4%
0.053731
 
0.4%
1.133387
 
0.3%
1.153342
 
0.3%
1.033319
 
0.3%
0.913313
 
0.3%
Other values (1542)938169
90.0%
ValueCountFrequency (%)
060212
5.8%
0.0111568
 
1.1%
0.026531
 
0.6%
0.035014
 
0.5%
0.044188
 
0.4%
0.053731
 
0.4%
0.063262
 
0.3%
0.072886
 
0.3%
0.082696
 
0.3%
0.092623
 
0.3%
ValueCountFrequency (%)
33.561
< 0.1%
31.231
< 0.1%
31.021
< 0.1%
29.781
< 0.1%
28.661
< 0.1%
28.461
< 0.1%
27.91
< 0.1%
27.751
< 0.1%
27.291
< 0.1%
27.261
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct554
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2624645513
Minimum0
Maximum7.44
Zeros64432
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:12.618986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.22
Q30.38
95-th percentile0.68
Maximum7.44
Range7.44
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2568259785
Coefficient of variation (CV)0.9785168215
Kurtosis54.92772656
Mean0.2624645513
Median Absolute Deviation (MAD)0.15
Skewness4.396544745
Sum273691.21
Variance0.06595958322
MonotonicityNot monotonic
2022-11-02T12:05:12.904921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064432
 
6.2%
0.0156248
 
5.4%
0.0231973
 
3.1%
0.0324474
 
2.3%
0.0421424
 
2.1%
0.0519764
 
1.9%
0.1719349
 
1.9%
0.1919343
 
1.9%
0.1819309
 
1.9%
0.2119217
 
1.8%
Other values (544)747241
71.7%
ValueCountFrequency (%)
064432
6.2%
0.0156248
5.4%
0.0231973
3.1%
0.0324474
 
2.3%
0.0421424
 
2.1%
0.0519764
 
1.9%
0.0618963
 
1.8%
0.0718404
 
1.8%
0.0818260
 
1.8%
0.0918107
 
1.7%
ValueCountFrequency (%)
7.441
< 0.1%
7.371
< 0.1%
7.051
< 0.1%
6.991
< 0.1%
6.851
< 0.1%
6.811
< 0.1%
6.791
< 0.1%
6.731
< 0.1%
6.581
< 0.1%
6.571
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.6%
Missing45338
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean181.3481154
Minimum0
Maximum360
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:13.040026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.23
Q191.77
median179.71
Q3270.61
95-th percentile329.78
Maximum360
Range360
Interquartile range (IQR)178.84

Descriptive statistics

Standard deviation98.50140569
Coefficient of variation (CV)0.5431620034
Kurtosis-1.355062494
Mean181.3481154
Median Absolute Deviation (MAD)89.38
Skewness0.003239643568
Sum180883138.9
Variance9702.526922
MonotonicityNot monotonic
2022-11-02T12:05:13.161225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901927
 
0.2%
280.7298
 
< 0.1%
264.0397
 
< 0.1%
273.7396
 
< 0.1%
267.1996
 
< 0.1%
90.7695
 
< 0.1%
87.1895
 
< 0.1%
92.1994
 
< 0.1%
93.9194
 
< 0.1%
268.3193
 
< 0.1%
Other values (35991)994651
95.4%
(Missing)45338
 
4.3%
ValueCountFrequency (%)
011
< 0.1%
0.0111
< 0.1%
0.0210
< 0.1%
0.0322
< 0.1%
0.0414
< 0.1%
0.0513
< 0.1%
0.0620
< 0.1%
0.0721
< 0.1%
0.0817
< 0.1%
0.0917
< 0.1%
ValueCountFrequency (%)
36010
< 0.1%
359.9910
< 0.1%
359.9812
< 0.1%
359.9721
< 0.1%
359.968
 
< 0.1%
359.959
< 0.1%
359.9420
< 0.1%
359.939
< 0.1%
359.9210
< 0.1%
359.9117
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.6%
Missing45338
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.8773805
Minimum0
Maximum360
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2022-11-02T12:05:13.292276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.5
Q192.09
median180.54
Q3270.38
95-th percentile335.49
Maximum360
Range360
Interquartile range (IQR)178.29

Descriptive statistics

Standard deviation100.2772581
Coefficient of variation (CV)0.5543935776
Kurtosis-1.275577476
Mean180.8773805
Median Absolute Deviation (MAD)89.16
Skewness-0.01011826178
Sum180413610.9
Variance10055.52849
MonotonicityNot monotonic
2022-11-02T12:05:13.417416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265.9272
 
< 0.1%
271.0572
 
< 0.1%
270.7270
 
< 0.1%
270.6968
 
< 0.1%
85.8868
 
< 0.1%
270.467
 
< 0.1%
266.3866
 
< 0.1%
93.0366
 
< 0.1%
270.7866
 
< 0.1%
274.4566
 
< 0.1%
Other values (35991)996755
95.6%
(Missing)45338
 
4.3%
ValueCountFrequency (%)
022
< 0.1%
0.0120
< 0.1%
0.0220
< 0.1%
0.0319
< 0.1%
0.0424
< 0.1%
0.0517
< 0.1%
0.0621
< 0.1%
0.0720
< 0.1%
0.0826
< 0.1%
0.0914
< 0.1%
ValueCountFrequency (%)
36016
< 0.1%
359.9920
< 0.1%
359.9825
< 0.1%
359.9720
< 0.1%
359.9627
< 0.1%
359.9527
< 0.1%
359.9425
< 0.1%
359.9333
< 0.1%
359.9216
< 0.1%
359.9120
< 0.1%

event
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
None
962711 
ball_snap
 
24426
pass_forward
 
21390
autoevent_passforward
 
10856
autoevent_ballsnap
 
9982
Other values (18)
 
13409

Length

Max length25
Median length4
Mean length4.676231858
Min length3

Characters and Unicode

Total characters4876253
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None962711
92.3%
ball_snap24426
 
2.3%
pass_forward21390
 
2.1%
autoevent_passforward10856
 
1.0%
autoevent_ballsnap9982
 
1.0%
play_action6118
 
0.6%
run1518
 
0.1%
qb_sack1357
 
0.1%
pass_arrived1357
 
0.1%
man_in_motion621
 
0.1%
Other values (13)2438
 
0.2%

Length

2022-11-02T12:05:13.543626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none962711
92.3%
ball_snap24426
 
2.3%
pass_forward21390
 
2.1%
autoevent_passforward10856
 
1.0%
autoevent_ballsnap9982
 
1.0%
play_action6118
 
0.6%
run1518
 
0.1%
qb_sack1357
 
0.1%
pass_arrived1357
 
0.1%
man_in_motion621
 
0.1%
Other values (13)2438
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1029549
21.1%
o1024696
21.0%
e1010045
20.7%
N962711
19.7%
a173512
 
3.6%
s106628
 
2.2%
_79212
 
1.6%
p76843
 
1.6%
l75555
 
1.5%
r70426
 
1.4%
Other values (15)267076
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3834330
78.6%
Uppercase Letter962711
 
19.7%
Connector Punctuation79212
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1029549
26.9%
o1024696
26.7%
e1010045
26.3%
a173512
 
4.5%
s106628
 
2.8%
p76843
 
2.0%
l75555
 
2.0%
r70426
 
1.8%
t53153
 
1.4%
b36041
 
0.9%
Other values (13)177882
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
N962711
100.0%
Connector Punctuation
ValueCountFrequency (%)
_79212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4797041
98.4%
Common79212
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1029549
21.5%
o1024696
21.4%
e1010045
21.1%
N962711
20.1%
a173512
 
3.6%
s106628
 
2.2%
p76843
 
1.6%
l75555
 
1.6%
r70426
 
1.5%
t53153
 
1.1%
Other values (14)213923
 
4.5%
Common
ValueCountFrequency (%)
_79212
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4876253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1029549
21.1%
o1024696
21.0%
e1010045
20.7%
N962711
19.7%
a173512
 
3.6%
s106628
 
2.2%
_79212
 
1.6%
p76843
 
1.6%
l75555
 
1.5%
r70426
 
1.4%
Other values (15)267076
 
5.5%

Interactions

2022-11-02T12:05:01.897644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:25.708208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:28.962902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:32.082884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:35.482581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:38.706394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:42.072965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:45.286135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:48.458910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:51.835310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:55.130683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:58.402750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:02.185977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:25.981982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:29.226184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:32.358354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:35.761229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:38.982304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:42.345429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:45.556513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:48.730119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:52.114458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:55.407799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:58.686001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:02.457234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:26.244415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:04:26.511810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:29.736967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:04:26.787637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:30.003988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:33.302678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:36.560111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:39.772202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:43.152318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:46.343561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:49.554357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:52.936502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:56.218741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:59.666099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:03.292868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:27.055469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:30.257914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:33.566135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:36.830575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:40.031988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:43.407894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:46.603262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:49.815014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:53.203371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:56.500116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:59.946223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:03.568220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:27.324251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:30.513235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:33.845566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:37.096023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:40.295475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:43.672011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:46.864053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:50.079262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:53.475647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:56.767036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:00.220806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:04:34.113079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:37.358220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:40.561696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:43.933486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:47.126356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:50.492732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:53.752958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:57.031952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:00.491874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:04.120951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:27.855513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:31.025095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:34.390868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:37.623728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:40.831119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:44.201127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:04:50.751629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:54.018973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:57.298308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:00.766332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:04.399966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:28.130987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:31.283442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:34.669084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:37.891602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:41.106882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:44.469834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:47.652024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:51.018239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:05:01.055484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:04.682299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:28.407579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:31.548405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:34.945558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:38.165374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:41.380033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:44.740732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:47.924558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:51.293620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:54.578743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:57.842898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:01.335134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:04.964297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:28.689804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:31.818254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:35.217261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:38.437534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:41.810460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:45.011116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:48.193512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:51.569061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:54.865149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:04:58.125469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:05:01.618480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T12:05:13.643531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T12:05:13.794471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T12:05:13.940336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T12:05:14.085998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T12:05:14.220261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T12:05:14.328457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T12:05:05.534250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T12:05:06.736952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T12:05:08.492921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T12:05:09.138256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
020210916006540031.012021-09-17T00:23:09.60023.0NYGright46.3222.360.930.830.09271.7179.32None
120210916006540031.022021-09-17T00:23:09.70023.0NYGright46.4322.391.071.050.11275.9272.33None
220210916006540031.032021-09-17T00:23:09.80023.0NYGright46.5422.441.211.110.12278.8567.03None
320210916006540031.042021-09-17T00:23:09.90023.0NYGright46.6522.491.321.140.13282.4562.63None
420210916006540031.052021-09-17T00:23:10.00023.0NYGright46.7722.561.491.420.14285.5459.26None
520210916006540031.062021-09-17T00:23:10.10023.0NYGright46.9122.651.781.670.17292.0856.87ball_snap
620210916006540031.072021-09-17T00:23:10.20023.0NYGright47.0722.751.941.490.18298.7154.79None
720210916006540031.082021-09-17T00:23:10.30023.0NYGright47.2322.872.171.500.21303.4153.02None
820210916006540031.092021-09-17T00:23:10.40023.0NYGright47.4223.022.381.390.23305.3251.86None
920210916006540031.0102021-09-17T00:23:10.50023.0NYGright47.6223.182.621.430.26306.3150.09None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
104276420210920003759NaN472021-09-21T03:07:54.400NaNfootballright67.8130.725.116.080.45NaNNaNNone
104276520210920003759NaN482021-09-21T03:07:54.500NaNfootballright68.0431.195.486.130.53NaNNaNNone
104276620210920003759NaN492021-09-21T03:07:54.600NaNfootballright68.3431.675.835.380.57NaNNaNNone
104276720210920003759NaN502021-09-21T03:07:54.700NaNfootballright68.6932.166.154.560.60NaNNaNNone
104276820210920003759NaN512021-09-21T03:07:54.800NaNfootballright69.0832.656.424.090.63NaNNaNpass_forward
104276920210920003759NaN522021-09-21T03:07:54.900NaNfootballright69.5233.136.623.070.65NaNNaNautoevent_passforward
104277020210920003759NaN532021-09-21T03:07:55.000NaNfootballright70.0933.567.122.610.71NaNNaNNone
104277120210920003759NaN542021-09-21T03:07:55.100NaNfootballright75.2335.4722.950.865.48NaNNaNNone
104277220210920003759NaN552021-09-21T03:07:55.200NaNfootballright77.3536.4322.871.262.33NaNNaNNone
104277320210920003759NaN562021-09-21T03:07:55.300NaNfootballright79.4237.3922.791.592.29NaNNaNNone